“Business intelligence has “invaded” the operational space in a big way, offering in-line analytics, real-time or near real-time decision-making support for all employees in the enterprise.”

She made some good points, especially on some of the challenges of upgrading your data infrastructure to cope with being more real time and more available so as to support operational processes but she left me wondering if the same old tools, upgraded, were really going to help. Would it really make your company act more intelligently, at an operational level, if you gave ever more people the same kinds of software tools? While wondering about this I saw Curt MonashRob Meredith post this one on The Myth of BI for the Masses in which he took issue with the idea that a single BI infrastructure could really support all the decision makers in a company. He felt that different investments might work better than broad deployment of the same tools. He also made a great point, as he said that the real value for BI comes “if the decision is a strategic one (the real sweet-spot for BI)”. His point was that these kinds of strategic decisions require a powerful but ad-hoc collection of decision support tools rather than a broad-based BI framework. This made me think about the kinds of problems that do not meet his definition and I came back to the whole idea of operational BI. Looking back to the quote from Claudia’s piece I have to wonder if the need is for decision-making by the systems of the enterprise rather than the employees. Not supporting more people in making decisions but automating more of those decisions instead. Like CurtRob, I think the power of BI comes in helping with bigger, less structured decisions both in terms of seeing what might happen and reporting on what actually happened. Reports, dashboards, cubes – none of this seems very helpful when you are considering operational processes. Partly this is because these operational processes often require automation and that means that the decisions within them must be made by systems not employees. Partly this is because the staff who are involved are not analytically trained or inclined, making it hard to see how they could use these tools anyway.

I was gearing up to write a post on this when, over on the Enterprise Resilience Management Blog, there was this post on Algorithms and Business which reiterated that “mathematical models are better than humans in making a number of predictions” (as Ian Ayres noted in Super Crunchers). This was a great post on the power of algorithms, predictive analytics and data mining that is, to make better decisions than people once there is any kind of data history to use. Indeed, studies even show that a human supported by an algorithm, while still doing better than an unassisted human, does not do as well as the algorithm alone (the post also referenced this article). This trend to use technology, and algorithms, to automate more decision making is the core of enterprise decision management – automate and improve the operational decisions that drive your business. Three quotes really struck me in this article:

“The newest space, and the one that’s most exciting, is where machines are actually in charge, but they have enough awareness to seek out people to help them when they get stuck,” he said – for example, when making “particularly complex, novel, or risky” decisions.” This is the classic use of what we call enterprise decision management. Handling the vast majority of decisions of a certain type while referring the remaining exceptions to a human for review. Insurance underwriting, claims processing, fraud detection and originations.

“embedded business logic allows organizations “to codify and centralize its hard-won knowledge in a concrete and easily transferable form, so it stays put when the experts move on.” This is one of the key values of using business rules, and predictive analytic models, to automate a decision. As the population ages and more and more knowledgeable workers retire, companies can no longer rely on people to make decisions even if they want to. There simply will not be enough people with the right skills as the boomers retire and, even if there were, it is not obvious that the new generation would want to do the same job or that the companies could reliably train all these new workers in the necessary skills. Decision automation and management may well be the only option.

“As far as most businesses are concerned, these problems typically fall into two types: improving various processes, such as how a network is configured and a supply chain is run, or analysing (sic) data on things such as customer spending.” I think the range of decisions to which this approach is being applied is growing rapidly. Not only do companies now have more and more data to analyze, they are also automating more processes and they want more of those processes to run without human intervention. It is no longer just that a company will analyze customer spending, now they will use that analysis to drive decisions that affect how a customer is treated, what offers get made to them, what price is used. Real-time embedded decisions.

Just when I thought I was done I saw that Jill Dyche had this post SAS and Teradata Show Their Hand in Vegas in which she referenced the new alliance between SAS and Teradata. One of the most interesting areas for me in this alliance is that the two companies will work to ensure that SAS analytic models can be executed, with great performance, in the Teradata engine. This means that the combination of the two firms is more powerful than ever for the delivery of enterprise decision management solutions. Rapid, in-line execution of analytic models is a critical component of the more sophisticated decision management solutions I see.

So, if you are trying to build an intelligent business, your way forward may not be to focus on your BI tools and on how to get more people to use them. Instead, you focus should perhaps be on how you can turn your data into algorithms that can drive your systems to make decisions automatically. To make your systems smart enough.

Perhaps much of the organizational struggle around how decisions are made grows from the often denied or ignored reality that we don’t agree on what problem or business issue we’re trying to solve by the decision. Until people agree on the problem and its cause, it is unlikely that any amount of data or robust process will result in timely and effective decisions.

Paul Nutt’s research from Ohio State (Why Decisions Fail) presents an overall dismal view of the effectiveness of most corporate decisions.

You’ve confused me for Curt Monash. I run the Monash BI Blog which is a blog of the BI research group at Monash University (in Australia). Confusingly, Curt is well known in BI circles, so the mistake was bound to happen at some stage!

I’m the author of the Myth of BI for the Masses post.

A great write-up though. I agree that there is probably some scope for embedding some decision making processes within systems (this happens anyway though, with any systems design – business rules have always been part of systems analysis). I’m somewhat skeptical of the possibility of full automation – and wary of the problems when the system gets it wrong. Claims from the AI community have been along these lines since the 1960s (Herb Simon famously claimed that we’d all be living in a bucolic paradise while machines did all the work by 1970).

No system can make decisions as well as an informed, competent human decision-maker. There’s also ethical issues associated with accountability and moral responsibility for individual decisions if no human was involved.

In any case, I do see a big difference between operational reporting needs (these are much easier to solve) compared with strategic decision-making needs. The BI vendors, generally, though see their tools as a universal solution. It’s a view based more on a desire to sell software licenses than on an accurate understanding of the cognitive and organisational factors involved in making an important decision.

Just another followup – a common misconception is the the ‘I’ in BI refers to intelligence in the sense of ‘smart’ or ‘insightful’. It’s actually used in the same sense as ‘military intelligence’, that is, the gathering and use of information to support strategic planning and decision-making. It springs from Herb Simon’s commonly cited model of decision-making consisting of intelligence gathering (ie understanding the decision problem), designing a set of solutions, and choosing a course of action from on of the solutions identified.

In other words, an intelligent business is a very different beast to a business that uses business intelligence.

I would disagree with the statement that “No system can make decisions as well as an informed, competent human decision-maker.” We can look to computer chess for an example where the computer routinely makes better decisions than even the most skilled human. Business strategy is a lot more complex than chess, due to incomplete information about the future and a broader range of possible choices.

That said, I do think people always need to be in the loop. The computer only does what you tell it: given a goal and some information, it will make choices which maximize the goal. But somebody needs to make sure the computer has the right things to choose from, is getting the right information, and has the right goal. Indeed, these things change over time (often fairly rapidly) and people will need to monitor any decision system to ensure it’s behaving in the intended manner.